Algorithms for extracting motifs from biological weighted sequences

Abstract

In this paper we present three algorithms for the Motif Identification Problem in Biological Weighted Sequences. The first algorithm extracts repeated motifs from a biological weighted sequence. The motifs correspond to repetitive words which are approximately equal, under a Hamming distance, with probability of occurrence 1/k, where k is a small constant. The second algorithm extracts common motifs from a set of N 2 weighted sequences. In this case, the motifs consists of words that must occur with probability 1/k, in 1 q N distinct sequences of the set. The third algorithm extracts maximal pairs from a biological weighted sequence. A pair in a sequence is the occurrence of the same word twice. In addition, the algorithms presented in this paper improve previous work on these problems.

A Dynamic Approach to Weighted Suffix Tree Construction Algorithm

“…The weighted suffix tree for a given weighted X=X [1]X [2]...X[n], of length n can be built by following the steps given below.…”

“…The molecular weighted sequence shown in ( Figure. 1) is found in the numerous applications of computational molecular biology [1] and it is defined as sequence of either nucleotides or amino acids, where each character in every position is assigned a certain weight. In computational biology few important biological processes such as DNA assembly process [12], pattern matching, and identification of repeated patterns in biological weighted sequences are modeled by molecular weighted sequences and also very help full in the translation of gene expression and regulation.…”

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

“…They may correspond to approximate repetitions randomly dispersed along the sequence, or to repetitions that occur in a periodic or approximately periodic fashion. The length and number of repeated elements one wishes to be able to identify may be highly variable [18].…”

Parallelization of Weighted Sequence Comparision By Using EBWT

“…The same technique adopted to find out mutations that triggers a disease and is also a substantial part of tracing the evolution of a certain organism [2]. The dynamic programming based smith-waterman algorithm is considered as the only comparison algorithm guaranteed that return an optimal result which is suitable for both of protein and DNA sequences [4]. However, this algorithm took considerable amount of time even to compare two small length sequences and also not suitable for molecular weighted sequences.…”

“…Thus, we try to alleviate the limitation of this algorithm in terms of its execution time by implementing extended burrow wheeler transform based molecular weighted sequence comparison algorithm. 13 The implementation of this intuitive idea for large weighted molecular sequence [4] can have take enormous amount of computation time and memory. This can be proved by considering a practical example of comparing molecular weighted sequence of 300 positions with molecular weighted pattern of 10 positions and in each position there is a possibility of four characters.…”